Modelling the climate change and cotton yield relationship in Mississippi: Autoregressive distributed lag approach

• Cotton-climate link assessed on historical data (1970–2020) for Mississippi (MS). • Climate change trend was quantified during cotton growing season. • A 1 °C rise in maximum temperature (Tmax) reduced cotton yield by 6.1 %. • A 1 °C rise in Tmin (minimum temperature) improved cotton yield by 5.5 %. • Tmin (79.6%) contributed more than Tmax (20.4%) to MS-climate warming. Development of mitigation strategies to combat climate change necessitates an advanced analysis of the historical connection between crops and climate. Such an analysis is lacking for the cotton ( Gossypium hirsutum L.)-climate research in Mississippi (MS). Hitherto, research has been confined to small-scale experimental settings, leaving an opportunity to explore large-scale inferences. Therefore, the present study aimed to compute MS climatic trends during the cotton growing period (CGP) from 1970 to 2020 using the Mann-Kendall and Sen slope methods. The impact of climate change on MS cotton yield was assessed using the autoregressive distributed lag (ARDL) model. The climatic variables considered were maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), precipitation (PR), and CO 2 emissions (COE). A required series of statistical tests, including pre- and post-analysis, model robustness, and goodness-of-fit were performed, and data met all criteria. Results revealed that Tmin (79.6 %) contributed more than Tmax (20.4 %) to the MS-climate warming over CGP. From 1970 to 2020, the Tmax, Tmin, DTR, and PR changed by + 0.30 °C, +1.17 °C, −1.07 °C, and + 22.54 mm, respectively, exhibiting change rate per decade of + 0.06 °C, +0.23 °C, −0.21 °C, and + 4.42 mm, respectively. Precipitation had no effect on cotton yield in the long or short-term. However, cotton yield significantly decreased with a rise in Tmax, and increased with a rise in Tmin and COE in the long-term. Conclusively, a 1 °C increase in Tmax reduced cotton yield by 6.1 %, a 1 °C increase in Tmin improved it by 5.5 %, and a unit increase in COE increased it by 0.45 % over the long run. Overall, the crop-climate link in MS cotton marked a varied sensitivity towards short and long-term, indicating the need to reassess current mitigation strategies. Additionally, testing the best agronomic practices in a controlled environment at the actual rates of climate change identified by the current study could provide cotton stakeholders with more precise and valuable insights.

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Publication Details

Journal
Ecological Indicators
Published
2024-09-01
DOI
https://doi.org/10.1016/j.ecolind.2024.112573
Citations
8
Primary Topic
Climate change impacts on agriculture
Type
article
Field-Weighted Citation Impact
8.23

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article

Modelling the climate change and cotton yield relationship in Mississippi: Autoregressive distributed lag approach

Vaughn Reed, Jagmandeep Dhillon, K. Raja Reddy, Darrin M. Dodds et al.
8 citations
Ecological Indicators
Climate change impacts on agriculture
8.23
article

Modelling the climate change and cotton yield relationship in Mississippi: Autoregressive distributed lag approach

Vaughn Reed, Jagmandeep Dhillon, K. Raja Reddy, Darrin M. Dodds, Pushp Kumar, Ramandeep Kumar Sharma, Krishna N. Reddy
article en
8 citations

Abstract

• Cotton-climate link assessed on historical data (1970–2020) for Mississippi (MS). • Climate change trend was quantified during cotton growing season. • A 1 °C rise in maximum temperature (Tmax) reduced cotton yield by 6.1 %. • A 1 °C rise in Tmin (minimum temperature) improved cotton yield by 5.5 %. • Tmin (79.6%) contributed more than Tmax (20.4%) to MS-climate warming. Development of mitigation strategies to combat climate change necessitates an advanced analysis of the historical connection between crops and climate. Such an analysis is lacking for the cotton ( Gossypium hirsutum L.)-climate research in Mississippi (MS). Hitherto, research has been confined to small-scale experimental settings, leaving an opportunity to explore large-scale inferences. Therefore, the present study aimed to compute MS climatic trends during the cotton growing period (CGP) from 1970 to 2020 using the Mann-Kendall and Sen slope methods. The impact of climate change on MS cotton yield was assessed using the autoregressive distributed lag (ARDL) model. The climatic variables considered were maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), precipitation (PR), and CO 2 emissions (COE). A required series of statistical tests, including pre- and post-analysis, model robustness, and goodness-of-fit were performed, and data met all criteria. Results revealed that Tmin (79.6 %) contributed more than Tmax (20.4 %) to the MS-climate warming over CGP. From 1970 to 2020, the Tmax, Tmin, DTR, and PR changed by + 0.30 °C, +1.17 °C, −1.07 °C, and + 22.54 mm, respectively, exhibiting change rate per decade of + 0.06 °C, +0.23 °C, −0.21 °C, and + 4.42 mm, respectively. Precipitation had no effect on cotton yield in the long or short-term. However, cotton yield significantly decreased with a rise in Tmax, and increased with a rise in Tmin and COE in the long-term. Conclusively, a 1 °C increase in Tmax reduced cotton yield by 6.1 %, a 1 °C increase in Tmin improved it by 5.5 %, and a unit increase in COE increased it by 0.45 % over the long run. Overall, the crop-climate link in MS cotton marked a varied sensitivity towards short and long-term, indicating the need to reassess current mitigation strategies. Additionally, testing the best agronomic practices in a controlled environment at the actual rates of climate change identified by the current study could provide cotton stakeholders with more precise and valuable insights.

Ecological IndicatorsVol. 166
Agricultural Research Service (US), United States Department of Agriculture (US), Mississippi State University (US), University of Rajasthan (IN)
Mississippi Agricultural and Forestry Experiment Station, Mississippi State University
Climate action
Openalex Percentile: Top 3%
Climate change impacts on agriculture
8.23
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